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Banks must capitalize on the opportunities small and medium enterprise (SME) lending offers.
SME lending has become a profitable business for banks. According to the Federal Deposit Insurance Corporation (FDIC), SMEs make up 99.9% of all US businesses and generate 47.5% of employment. And according to a World Bank report, the world’s micro, small, and medium-sized enterprises have unmet finance needs of approximately US$ 5.2 trillion a year, roughly 1.4 times the current level of lending for such enterprises.
Today, SME lenders process loans through departments in corporate banking, where the process is mostly manual and time-consuming.
Banks need to transform their SME lending process so that there is minimal manual involvement while complying with mandatory credit and quality checks. Currently, there are several challenges in processing SME loans. Topmost among them are:
Cloud-native services can help lenders digitalize and automate SME lending for improved customer experience and faster processing.
An enterprise-wide, end-to-end digitalization of the SME lending process can help overcome existing challenges. Digitalized business management workflows, including loan origination capabilities, can help banks close more loans and generate more opportunities.
Automating the customer journey using cloud technologies can streamline SME lending processes and elevate customer and employee experience. All over the world, many SME businesses are already adopting cloud solutions and leveraging serverless computing with scalability to enable their SME businesses to move fast and quickly meet customer requirements.
Cloud and automation
Using cloud and machine learning (ML)-based solutions to taking credit decisions.
With cloud solutions, banks can classify and extract the required information from different customer documents—including credit reports, bank and income statements, and balance sheets. The extracted information can be validated by leveraging the banks’ customer relationship management (CRM) systems to avoid fraudulent activities.
Cloud can also help with challenges around traditional credit decisions. Today, many banks are dependent on relationship managers or underwriters who make subjective decisions. With advanced automated credit decisioning models, banks can understand customer behavior, increase revenue, and reduce credit losses. And leveraging ML-powered cloud solutions, they can build models to assess lending risks and enhance the performance of credit models.
Moreover, the lending process involves loan origination, risk evaluation, credit decisions, underwriting, collateral management, debt collection, loan servicing, and reporting. Making it easier for banks are several AI and ML-powered solutions that enable digital loan origination and risk management, automated financial spreading and data extraction, and workflow management. Not just that, these solutions also enhance the effectiveness and efficiency of communication with the customer throughout the loan cycle—from providing the status of the application to final approval and disbursement.